Text Generation
Transformers
Safetensors
gpt2
safety
alignment
preference-learning
ppo
full
rlhf
text-generation-inference
Instructions to use OmAhire369/safe-genai-ppo-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OmAhire369/safe-genai-ppo-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OmAhire369/safe-genai-ppo-full")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OmAhire369/safe-genai-ppo-full") model = AutoModelForCausalLM.from_pretrained("OmAhire369/safe-genai-ppo-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OmAhire369/safe-genai-ppo-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmAhire369/safe-genai-ppo-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmAhire369/safe-genai-ppo-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OmAhire369/safe-genai-ppo-full
- SGLang
How to use OmAhire369/safe-genai-ppo-full with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OmAhire369/safe-genai-ppo-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmAhire369/safe-genai-ppo-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "OmAhire369/safe-genai-ppo-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmAhire369/safe-genai-ppo-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OmAhire369/safe-genai-ppo-full with Docker Model Runner:
docker model run hf.co/OmAhire369/safe-genai-ppo-full
Download value_head.pt from OmAhire369/safe-genai-ppo-full: direct link, hf CLI and curl.
- Browser
- Download file 6.08 kB
-
https://huggingface.co/OmAhire369/safe-genai-ppo-full/resolve/main/value_head.pt
- Command line
-
hf download hf://OmAhire369/safe-genai-ppo-full/value_head.pt
-
curl -L -o value_head.pt https://huggingface.co/OmAhire369/safe-genai-ppo-full/resolve/main/value_head.pt
6.08 kB
- Xet hash:
- 2af2907d00f90215e79f9e070b15b3b1c47c4c524e8f5a860a7667acb3b54ccd
- Size of remote file:
- 6.08 kB
- SHA256:
- 41fcd877ea209cc3eeb14e5001cc45e57be0d4559b87b94b2107aff25d06e12b
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